Embracing the 3-Month Rule: A Pragmatic Approach to Development

In the startup world, the adage “do things that don’t scale” championed by Paul Graham is often echoed, yet the practical implementation of this advice in coding remains under-discussed. As I reflect on my journey of developing an AI podcast platform over the past eight months, I’ve established a strategic framework that I call the “3-Month Rule.” This approach involves giving every non-scalable solution a lifespan of three months. After this period, the solution must either demonstrate its worth to warrant further investment or be discarded.

The Challenge of Scalability in Startups

As software engineers, we are conditioned to gravitate toward scalable architectures from the outset—think design patterns, microservices, and distributed systems. These elements are ideal for companies with massive user bases. However, in a startup environment, preemptively crafting scalable solutions can be an expensive detour. More often than not, it’s about preparing for hypothetical users while neglecting the immediate needs of the ones we currently serve. My 3-month rule compels me to focus on writing straightforward, albeit imperfect, code that can be deployed quickly. This practice ultimately helps me gather invaluable insights regarding user requirements.

My Current Infrastructure Hacks: Lessons Learned

1. Consolidation on One Virtual Machine (VM)
   Currently, my entire infrastructure, including the database, web server, and background jobs, operates on a single $40/month VM. While this approach sacrifices redundancy, it has provided remarkably clear insights into my true resource needs. Over the past two months, I’ve discovered that my supposedly “AI-heavy” platform only requires 4GB of RAM at peak times. The complex Kubernetes architecture I initially considered would have merely involved managing empty containers. Moreover, each crash—my system has faced two so far—has revealed unexpected points of failure that offer essential learning opportunities.

2. Hardcoded Configuration Values
   Instead of relying on configuration files or environment variables, I’ve opted for hardcoded constants scattered throughout my codebase. This method might seem unorthodox, but it simplifies the process of tracking changes in version control. In the last three months, I’ve altered configuration values only three times, which translates to a mere 15 minutes of redeployment compared to the 40 hours that would have been necessary to establish a formal configuration management system.

3. Utilizing SQLite for Production
   Surprisingly, my multi